About this course

Entry requirements

The typical entry requirement is a Bachelors degree (with Honours) at 2:1 level or better in an appropriate field of study. Individual consideration is given to mature students with significant and relevant experience and with professional qualifications. Applications from international students are welcome. International qualifications will be evaluated in line with the National Recognition Information Centre (NARIC) guidelines.

Months of entry

September

Course content

This programme aims to develop a high level understanding of quantitative and computational geographical methods. This includes skills in GIS software and statistical programming languages, such as R or Python.

Within an applied setting, emphasis is placed on developing skills in the visualisation, modelling and statistical analysis of spatial data using both web-based and traditional techniques.

Human activity are increasingly associated with the generation of large volumes of data. For example, transactional data are collated by retailers for marketing and store location purposes, administrative data are assembled to help with the efficient running of public services, data shadows are created through social media use, and an increased prevalence of smart-card linked transport systems record our travel behaviours.

Many grand human challenges concern problems of a geographical nature; be this how we can mitigate the human impact of climate change; ensure global food and water security; design energy systems that are resilient within the context of future population dynamics; or, how to design future cities where spatial inequities in health and wellbeing might be eradicated? The growing volumes of big data about the form, function and dynamics of human activities and their contexts are providing new opportunities to advance such debates within a framework of Geographic Data Science.